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The digital and material traceability of our interactions in organizations are nowadays the subject of very advanced analyses through tools known as social media analytics (SMA). As thinking (infrastructure), SMA tools constitute objects to think of our digitally mediated interactions with. It produces a substratum (a new meaning) that would not exist otherwise, and enacts different types of reasoning that hypothetically influence community managers’ or members’ sensemaking of digitally mediated interactions. This chapter proposes to look behind the curtain of charts and graphs, in order to highlight the performativity of the interactions between the different machines and the traces of our digitally mediated interactions. Drawing on a detailed analysis of the fabric of SMA, this chapter highlights the explanatory power of a communication perspective on types of reasoning enacted by thinking infrastructures. First, considering the SMA tool as an editorial enunciation allows us to see it as a process implying several beings (e.g. machines, humans and logs) that are not without consequences. Second, we show that these beings have different modalities of interactions with each other, and that these modalities of interactions influence the materiality of the digital traces of past interactions. Third, throughout the process, we demonstrate the fragility and variability of their materiality. Finally, faced with the rise of a technological deterministic discourse, which tends to portray the exploitation of our digital traces as an objective way of representing the collaborative practices that make up the organization, our research aims, on the contrary, to demonstrate their relativity.

The purpose of this article is to provide an overview of agent-based modeling as an alternative method for public administration research. It is focused on encouraging public administration scholars to come to better understanding of the method.

This article performed a comprehensive review of methodological issues relative to agent-based modeling.

After reviewing the current research themes in public administration and the methodological nature of agent-based modeling, we found that agent-based modeling can help researchers to advance theories by means of sophisticated thought experiment which is not possible by formal modeling and verbal reasoning. We also pointed out that agent-based modeling does not substitute empirical research, but can add much value through being part of a mixed-method and multidisciplinary research.

We suggested that interested researchers may need to take a strategic approach in developing and describing a pertinent model and reporting its results.

Agent-based modeling has rarely been used in public administration research. The article provides an introductory overview for researchers not familiar with ABM and suggests to the academic community future venues that would unfold from agent-based modeling.

This paper aims to serve two main purposes. In the first instance it aims to it provide an overview addressing the state‐of‐the‐art in the area of activity recognition, in particular, in the area of object‐based activity recognition. This will provide the necessary material to inform relevant research communities of the latest developments in this area in addition to providing a reference for researchers and system developers who ware working towards the design and development of activity‐based context aware applications. In the second instance this paper introduces a novel approach to activity recognition based on the use of ontological modeling, representation and reasoning, aiming to consolidate and improve existing approaches in terms of scalability, applicability and easy‐of‐use.

The paper initially reviews the existing approaches and algorithms, which have been used for activity recognition in a number of related areas. From each of these, their strengths and weaknesses are discussed with particular emphasis being placed on the application domain of sensor enabled intelligent pervasive environments. Based on an analysis of existing solutions, the paper then proposes an integrated ontology‐based approach to activity recognition. The proposed approach adopts ontologies for modeling sensors, objects and activities, and exploits logical semantic reasoning for the purposes of activity recognition. This enables incremental progressive activity recognition at both coarse‐grained and fine‐grained levels. The approach has been considered within the realms of a real world activity recognition scenario in the context of assisted living within Smart Home environments.

Existing activity recognition methods are mainly based on probabilistic reasoning, which inherently suffer from a number of limitations such as ad hoc static models, data scarcity and scalability. Analysis of the state‐of‐the‐art has helped to identify a major gap between existing approaches and the need for novel recognition approaches posed by the emerging multimodal sensor technologies and context‐aware personalised activity‐based applications in intelligent pervasive environments. The proposed ontology based approach to activity recognition is believed to be the first of its kind, which provides an integrated framework‐based on the unified conceptual backbone, i.e. activity ontologies, addressing the lifecycle of activity recognition. The approach allows easy incorporation of domain knowledge and machine understandability, which facilitates interoperability, reusability and intelligent processing at a higher level of automation.

The comprehensive overview and critiques on existing work on activity recognition provide a valuable reference for researchers and system developers in related research communities. The proposed ontology‐based approach to activity recognition, in particular the recognition algorithm has been built on description logic based semantic reasoning and offers a promising alternative to traditional probabilistic methods. In addition, activities of daily living (ADL) activity ontologies in the context of smart homes have not been, to the best of one's knowledge, been produced elsewhere.

Ontology in information studies consists of antinomic conceptions, methodologies, and emphases in both application and philosophizing. A comprehensive understanding of ontology in information studies can be achieved by employing Slavoj Žižek's parallax view which holds that reality is not only best understood by articulating conflicting perspectives on a particular phenomenon, but that given phenomena are fundamentally constrained by incommensurable perspectives that must be acknowledged accordingly. The paper aims to discuss these issues.

Ontology in information studies, including computational ontology development, is analyzed using critical information theory based on Heideggerian, poststructuralist, and anti-postmodern philosophy. The discussion is framed by Žižek's notion of the parallax Real.

A complete understanding of ontology in information studies that does not reduce ontology to a totalizing theory or sequester notions of ontology to conflicting, unrelated discourses, necessarily accepts articulating the alterity between differing ontological views as the means by which one can best allude to what “ontology in information studies really is.”

This paper presents a comprehensive analysis of radically different ontological perspectives on the nature of reality with respect to digital technology.

In this chapter, the authors critique dominant technocratic conceptions of rigor in management research and elaborate an alternative account of rigor that is rooted in methodology and involves a concern with the quality of scientific reasoning rather than a narrower focus on methods or measurement issues per se. Based on the proposed redefinition, the authors conceptualize how rigor, as an essential quality of reasoning, may be defined and the authors in turn qualify alternative methodological criteria for how they might assess the rigor of any particular piece of research. In short, with this chapter the authors’ overall aim is to shift the basis of rigor to an altogether more legitimate and commensurable notion that squarely puts the focus on reasoning and scientific inference for quantitative and qualitative research alike. The authors highlight some of the benefits that such an alternative and unified view of rigor may potentially provide toward fostering the quality and progress of management research.

We present an algorithm for contact resolution that is valid for a wide variety of polygonal two dimensional shapes and is of linear computational complexity. The algorithm is designed for use in discrete element analysis of granular and multibody systems exhibiting discontinuous behaviour. Contact detection usually consists of a spatial sorting phase and a contact resolution phase. The spatial sorting phase seeks to avoid an all‐to‐all body comparison by culling the number of objects which are potential contactors of a given object. The contact resolution phase resolves the details of the contact between two given objects. The algorithm presented here (called DFR) addresses the contact resolution phase and is applicable to convex geometries and to a restricted set of concave geometries. Examination of the algorithm establishes an upper bound linear computational complexity, of order O(N), with respect to the number of points (N) used to define the object boundary. The DFR algorithm is combined with a modified heapsort algorithm for spatial sorting of M bodies which has complexity O(M log M) and is applied to a baseline granular simulation problem to test its efficiency.

Introduces a case‐based management system (CBMS) for decision making such as allocating tasks under a distributed manufacturing environment. The distributed manufacturing environment can be viewed as a multi‐agent system in which each manufacturing plant is an agent to produce the whole or part of the products ordered by the customers. The knowledge is represented as cases in the system which can be reused to produce new solutions. This learn‐by‐experience approach provides a particular strength to case‐based reasoning over most other AI methods. The effectiveness of the proposed concept is demonstrated by applying the CBMS to a task allocation example. By using the CBMS approach, tasks or jobs can be assigned to appropriate agents effectively. Moreover, CBMS can be used locally or globally via WAN or Internet with high‐security protection as a knowledge management system in small and medium enterprises.

This paper aims to offer an innovative and original solution methodology proposal to the problem of arbitrary complex multiscale (ACM) ontological uncertainty management (OUM). The solution is based on the postulate that society is an ACM system of purposive actors within continuous change. Present social problems are multiscale-order deficiencies, which cannot be fixed by the traditional hierarchical approach alone, by doing what one does better or more intensely, but rather by changing the way one does it.

This paper treasures several past guidelines, from McCulloch, Wiener, Conant, Ashby and von Foerster to Bateson, Beer and Rosen’s concept of a non-trivial system to arrive to an indispensable and key anticipatory learning system (ALS) component for managing unexpected perturbations by an antifragility approach as defined by Taleb. This ALS component is the key part of our new methodology called “computational information conservation theory (CICT) OUM” approach, based on brand new numeric system behavior awareness from CICT.

To achieve an antifragility behavior, next generation system must use new CICT OUM-like approach to face the problem of multiscale OUM effectively and successfully. In this manner, homeodynamic operating equilibria can emerge out of a self-organizing landscape of self-structuring attractor points in a natural way.

This paper presents a relevant contribution toward a new post-Bertalanffy Extended Theory of Systems. Due to its intrinsic self-scaling properties, this system approach can be applied at any system scale: from single quantum system application development to full system governance strategic assessment policies and beyond.

The new post-Bertalanffy Extended Theory of Systems Framework allows, for the first time, social, biological and biomedical engineering ideal system categorization levels, from an operational perspective, to be matched exactly to practical system modeling interaction styles, with no paradigmatic operational ambiguity and information loss.

Even new social and advanced health and wellbeing information application can successfully and reliably manage higher system complexity than contemporary ones, with a minimum of design specification and less system final operative environment knowledge at design level. The present paper offers for discussion an innovative solution proposal for the complex society and big government modeling and management approach.

Specifically, advanced wellbeing applications, high reliability organization, mission critical project system, very low technological risk and crisis management system can benefit highly from our new methodology called CICT OUM approach and related techniques. This paper presents a relevant contribution toward a new post-Bertalanffy Extended Theory of Systems. Due to its intrinsic self-scaling properties, this system approach can be applied at any system scale: from a single quantum system application development to full system governance strategic assessment policies and beyond.

This paper aims to address some security issues in open systems such as service‐oriented applications and grid computing. It proposes a security framework for these systems taking a trust viewpoint. The objective is to equip the entities in these systems with mechanisms allowing them to decide about trusting or not each other before starting transactions.

In this paper, the entities of open systems (web services, virtual organizations, etc.) are designed as software autonomous agents equipped with advanced communication and reasoning capabilities. Agents interact with one another by communicating using public dialogue game‐based protocols and strategies on how to use these protocols. These strategies are private to individual agents, and are defined in terms of dialogue games with conditions. Agents use their reasoning capabilities to evaluate these conditions and deploy their strategies. Agents compute the trust they have in other agents, represented as a subjective quantitative value, using direct and indirect interaction histories with these other agents and the notion of social networks.

The paper finds that trust is subject to many parameters such as the number of interactions between agents, the size of the social network, and the timely relevance of information. Combining these parameters provides a comprehensive trust model. The proposed framework is proved to be computationally efficient and simulations show that it can be used to detect malicious entities.

The paper proposes different protocols and strategies for trust computation and different parameters to consider when computing this trust. It proposes an efficient algorithm for this computation and a prototype simulating it.